公开(公告)号：US20240085777A1

公开(公告)日：2024-03-14

申请号：US18326659

申请日：2023-05-31

Applicant: Samsung Electronics Co., Ltd.

Inventor： Dae Young PARK ,  Jeong Hoon KO ,  Seong Ryeol KIM ,  Young-Gu KIM ,  Tae Hoon KIM ,  Hyun Joong KIM ,  Young Ju LEE

IPC: G03F1/36 ,  G03F1/80 ,  G03F1/84 ,  G03F7/00 ,  G03F7/20

CPC classification number: G03F1/36 ,  G03F1/80 ,  G03F1/84 ,  G03F7/70625 ,  G03F7/70683 ,  G03F7/706841

Abstract: Provided is a process proximity effect correction method capable of efficiently improving the dispersion of patterns. There is a process proximity effect correction method according to some embodiments, the process proximity effect correction method of a process proximity effect correction device for performing process proximity effect correction (PPC) of a plurality of patterns using a machine learning module executed by a processor, comprising: training a sensitivity model by inputting a layout image of the plurality of patterns and a layout critical dimension (CD) of the plurality of patterns into the machine learning module; estimating an after cleaning inspection critical dimension (ACI-CD) sensitivity prediction value of the plurality of patterns by inferring an ACI-CD prediction value of the plurality of patterns; and determining a correction rate of the layout CD of the plurality of patterns using the estimated sensitivity prediction value.

公开(公告)号：US20190065630A1

公开(公告)日：2019-02-28

申请号：US15901358

申请日：2018-02-21

Applicant: Samsung Electronics Co., Ltd.

Inventor： Seong Ryeol KIM ,  Jeong Hoon KO ,  Seong Je KIM ,  Je Hyun LEE ,  Jong Wook JEON

IPC: G06F17/50 ,  G06N3/08 ,  G06F17/18 ,  G06N3/04 ,  H01L27/02

Abstract: A yield prediction apparatus is provided. The yield prediction apparatus may include at least one processor coupled to at least one non-transitory computer-readable medium. The at least one processor may be configured to receive a first variable associated with operating characteristics of a semiconductor device, perform a simulation for the operating characteristics of the semiconductor device, perform a neural network regression analysis using a result of the simulation to determine a first function for the first variable, and predict a yield of the semiconductor integrated circuit based on an advanced Monte Carlo simulation. An input of the advanced Monte Carlo simulation may include the determined first function.